HS project

IFE basal and ORS signature

This file is used to identify specific markers for ORS and IFE basal.

library(dplyr)
library(patchwork)
library(ggplot2)
library(ComplexHeatmap)

.libPaths()

## [1] "/usr/local/lib/R/library"

Preparation

In this section, we set the global settings of the analysis. We will store data there :

out_dir = "."

We load the dataset :

sobj = readRDS(paste0(out_dir, "/hs_hd_sobj.rds"))
sobj

## An object of class Seurat 
## 20003 features across 12111 samples within 1 assay 
## Active assay: RNA (20003 features, 2000 variable features)
##  6 dimensional reductions calculated: RNA_pca, RNA_pca_38_tsne, RNA_pca_38_umap, harmony, harmony_38_umap, harmony_38_tsne

We load the sample information :

sample_info = readRDS(paste0(out_dir, "/../1_metadata/hs_hd_sample_info.rds"))
project_names_oi = sample_info$project_name

graphics::pie(rep(1, nrow(sample_info)),
              col = sample_info$color,
              labels = sample_info$project_name)

Here are custom colors for each cell type :

color_markers = readRDS(paste0(out_dir, "/../1_metadata/hs_hd_color_markers.rds"))

data.frame(cell_type = names(color_markers),
           color = unlist(color_markers)) %>%
  ggplot2::ggplot(., aes(x = cell_type, y = 0, fill = cell_type)) +
  ggplot2::geom_point(pch = 21, size = 5) +
  ggplot2::scale_fill_manual(values = unlist(color_markers), breaks = names(color_markers)) +
  ggplot2::theme_classic() +
  ggplot2::theme(legend.position = "none",
                 axis.line = element_blank(),
                 axis.title = element_blank(),
                 axis.ticks = element_blank(),
                 axis.text.y = element_blank(),
                 axis.text.x = element_text(angle = 30, hjust = 1))

This is the projection of interest :

name2D = "harmony_38_tsne"

We design a custom function to make a histogram and a wordcloud to visualize differentially expressed genes :

hist_wc_fun = function(mark, col) {
  cut_colors = c("firebrick4", "firebrick2", "indianred1", "darksalmon",
                 "lightpink", "gray50", "khaki3", "darkolivegreen1",
                 "olivedrab1", "chartreuse2", "chartreuse4")
  cut_colors_cont = c(rev(RColorBrewer::brewer.pal(name = "Reds", n = 9)[c(5:9)]),
                      RColorBrewer::brewer.pal(name = "Greens", n = 9)[c(5:9)])
  
  if (col == "pct.1") {
    mark$pct = mark$pct.1
    mark$pct_cut = mark$pct.1_cut
  } else if (col == "pct.2") {
    mark$pct = mark$pct.2
    mark$pct_cut = mark$pct.2_cut
    cut_colors = rev(cut_colors)
    cut_colors_cont = rev(cut_colors_cont)
  } else {
    stop("col must be either pct.1 or pct.2")
  }
  
  p_hist = ggplot2::ggplot(mark, mapping = aes(x = avg_logFC, fill = pct_cut)) +
    ggplot2::geom_histogram(binwidth = 0.05) +
    ggplot2::scale_fill_manual(breaks = levels(mark$pct_cut),
                               values = cut_colors,
                               name = paste0(col, "_cut")) +
    ggplot2::theme_classic()
  
  p_wc = ggplot2::ggplot(mark, aes(label = gene_name, size = avg_logFC, color = pct)) +
    ggwordcloud::geom_text_wordcloud_area(show.legend = TRUE, seed = 1) +
    ggplot2::scale_color_gradientn(colors = cut_colors_cont,
                                   name = col) +
    ggplot2::scale_size_area(max_size = 5) +
    ggplot2::theme_minimal() +
    ggplot2::guides(size = "none")
  
  p = patchwork::wrap_plots(p_hist, p_wc, nrow = 1)
  
  return(p)
}

Visualization

Gene expression

We visualize gene expression for some markers :

features = c("percent.mt", "percent.rb", "nFeature_RNA")

plot_list = lapply(features, FUN = function(one_gene) {
  Seurat::FeaturePlot(sobj, features = one_gene,
                      reduction = name2D) +
    ggplot2::theme(aspect.ratio = 1) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
    Seurat::NoAxes()
})

patchwork::wrap_plots(plot_list, ncol = 3)

Cluster type Clusters and cell type

We visualize clusters and cell type :

cluster_plot = Seurat::DimPlot(sobj, group.by = "seurat_clusters",
                               reduction = name2D, label = TRUE) +
  ggplot2::labs(title = "Cluster ID") +
  Seurat::NoAxes() +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))

cell_type_plot = Seurat::DimPlot(sobj, group.by = "cell_type",
                                 reduction = name2D, label = FALSE) +
  ggplot2::scale_color_manual(values = color_markers,
                              breaks = names(color_markers)) +
  ggplot2::labs(title = "Cell type") +
  Seurat::NoAxes() +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))

cell_type_plot | cluster_plot

We summarize major cell type by cluster :

cell_type_clusters = sobj@meta.data[, c("cell_type", "seurat_clusters")] %>%
  table() %>%
  prop.table(., margin = 2) %>%
  apply(., 2, which.max)
cell_type_clusters = setNames(levels(sobj$cell_type)[cell_type_clusters],
                              nm = names(cell_type_clusters))

We define cluster type :

sobj$cluster_type = cell_type_clusters[sobj$seurat_clusters] %>%
  as.factor()
table(sobj$cluster_type, sobj$cell_type)

##                       
##                        CD4 T cells CD8 T cells Langerhans cells macrophages
##   B cells                        0           0                0           0
##   CD4 T cells                  774          52                2           1
##   CD8 T cells                   86         545                1           0
##   HF-SCs                         0           0                0           0
##   IFE basal                      2           0                0           0
##   IFE granular spinous           0           0                0           0
##   IRS                            0           0                0           0
##   Langerhans cells              15           0              253          51
##   ORS                            0           0                0           0
##   cortex                         0           0                0           0
##   cuticle                        1           0                0           0
##   macrophages                    9           0               26         423
##   medulla                        0           0                0           0
##   proliferative                  0           0                3           0
##   sebocytes                      0           0                0           0
##                       
##                        B cells cuticle cortex medulla  IRS proliferative  ORS
##   B cells                   37       0      0       1    0             0    0
##   CD4 T cells                3       2      0       3    3             2    1
##   CD8 T cells                4       0      0       0    0             0    0
##   HF-SCs                     2       4      0       2    2             9   41
##   IFE basal                  3       5      2      15    6            23   21
##   IFE granular spinous       1       1      0       0    0             2   25
##   IRS                        0       0      0       0  155             4    0
##   Langerhans cells           1       1      1       0    2            23    2
##   ORS                        1       4      0       5    1            23 1502
##   cortex                     0     105    199       4    0             0    0
##   cuticle                    0     804     29      21    0             1    0
##   macrophages                1       0      0       0    1             0    0
##   medulla                    1      26      1     335    0            17    0
##   proliferative             12     318      8      42  126          1384   44
##   sebocytes                  0       0      1       1    0             1    0
##                       
##                        IFE basal IFE granular spinous HF-SCs sebocytes
##   B cells                      0                    0      0         0
##   CD4 T cells                  0                    0      0         0
##   CD8 T cells                  0                    0      0         0
##   HF-SCs                     104                    3   1286         1
##   IFE basal                 1779                   57     44        16
##   IFE granular spinous        66                  498      7        13
##   IRS                          0                    0      0         0
##   Langerhans cells             0                    0      1         2
##   ORS                         11                  113     23         7
##   cortex                       0                    0      0         0
##   cuticle                      0                    0      0         2
##   macrophages                  0                    0      0         0
##   medulla                      0                    0      1         0
##   proliferative               75                   71     24        18
##   sebocytes                   63                    1      0       154

We compare cluster annotation and cell type annotation :

cell_type_plot

p2 = Seurat::DimPlot(sobj, group.by = "cluster_type",
                     reduction = name2D, cols = color_markers) +
  ggplot2::labs(title = "Cluster type") +
  Seurat::NoAxes() +
  ggplot2::theme(aspect.ratio = 1,
                 plot.title = element_text(hjust = 0.5))

patchwork::wrap_plots(cell_type_plot, p2, guides = "collect")

There is mis-annotation, so we keep the single-cell level cell type annotation.

Differential expression

In this section, we perform DE between interfollicular epidermis (IFE) basal or outer root sheath (ORS), and all remaining cells. We save the results in a list :

list_results = list()

We change cell identities to cell type :

Seurat::Idents(sobj) = sobj$cell_type

table(Seurat::Idents(sobj))

## 
##          CD4 T cells          CD8 T cells     Langerhans cells 
##                  887                  597                  285 
##          macrophages              B cells              cuticle 
##                  475                   66                 1270 
##               cortex              medulla                  IRS 
##                  241                  429                  296 
##        proliferative                  ORS            IFE basal 
##                 1489                 1636                 2098 
## IFE granular spinous               HF-SCs            sebocytes 
##                  743                 1386                  213

ORS markers

In this section, we compared ORS to other cells.

group_name = "ORS_vs_all"

aquarius::plot_red_and_blue(sobj,
                            group1 = "ORS",
                            reduction = name2D) +
  ggplot2::labs(title = group_name)

Differential expression

We identify specific markers for each population :

mark = Seurat::FindMarkers(sobj, ident.1 = "ORS")

mark = mark %>%
  dplyr::filter(p_val_adj < 0.05) %>%
  dplyr::arrange(-avg_logFC, pct.1 - pct.2)
mark$gene_name = rownames(mark)

list_results[[group_name]]$mark = mark

dim(mark)

## [1] 970   6

head(mark, n = 20)

##                  p_val avg_logFC pct.1 pct.2     p_val_adj gene_name
## KRT16     0.000000e+00  3.892906 0.905 0.155  0.000000e+00     KRT16
## KRT6B     0.000000e+00  3.166639 0.921 0.275  0.000000e+00     KRT6B
## KRT17     0.000000e+00  2.954873 0.980 0.529  0.000000e+00     KRT17
## KRT6A     0.000000e+00  2.860010 0.752 0.260  0.000000e+00     KRT6A
## S100A2    0.000000e+00  2.635463 0.992 0.789  0.000000e+00    S100A2
## KRT6C     0.000000e+00  2.494914 0.548 0.068  0.000000e+00     KRT6C
## KRT5      0.000000e+00  1.988478 0.993 0.749  0.000000e+00      KRT5
## FABP5     0.000000e+00  1.975885 0.989 0.668  0.000000e+00     FABP5
## GJB6      0.000000e+00  1.912305 0.908 0.525  0.000000e+00      GJB6
## KRT14     0.000000e+00  1.758304 0.992 0.699  0.000000e+00     KRT14
## NDUFA4L2  0.000000e+00  1.721714 0.680 0.212  0.000000e+00  NDUFA4L2
## CST6      0.000000e+00  1.666433 0.384 0.076  0.000000e+00      CST6
## GJB2      0.000000e+00  1.640163 0.826 0.503  0.000000e+00      GJB2
## HSPB1     0.000000e+00  1.630779 0.985 0.835  0.000000e+00     HSPB1
## TM4SF1    0.000000e+00  1.597284 0.801 0.243  0.000000e+00    TM4SF1
## CALML3    0.000000e+00  1.590650 0.958 0.685  0.000000e+00    CALML3
## SDC1      0.000000e+00  1.582781 0.829 0.544  0.000000e+00      SDC1
## S100A16   0.000000e+00  1.547152 0.955 0.720  0.000000e+00   S100A16
## ANXA2     0.000000e+00  1.518822 0.991 0.852  0.000000e+00     ANXA2
## LGALS7B  3.086968e-238  1.508193 0.748 0.465 6.174861e-234   LGALS7B

How many genes enriched in ORS ?

mark_ORS = mark %>%
  dplyr::filter(avg_logFC > 0)

nrow(mark_ORS)

## [1] 336

We cut pct.1 and pct.2 by bins of 0.1 :

mark_ORS$pct.1_cut = cut(mark_ORS$pct.1, breaks = 10)
mark_ORS$pct.2_cut = cut(mark_ORS$pct.2, breaks = 10)

head(mark_ORS)

##        p_val avg_logFC pct.1 pct.2 p_val_adj gene_name     pct.1_cut
## KRT16      0  3.892906 0.905 0.155         0     KRT16 (0.905,0.995]
## KRT6B      0  3.166639 0.921 0.275         0     KRT6B (0.905,0.995]
## KRT17      0  2.954873 0.980 0.529         0     KRT17 (0.905,0.995]
## KRT6A      0  2.860010 0.752 0.260         0     KRT6A (0.726,0.815]
## S100A2     0  2.635463 0.992 0.789         0    S100A2 (0.905,0.995]
## KRT6C      0  2.494914 0.548 0.068         0     KRT6C (0.547,0.637]
##              pct.2_cut
## KRT16      (0.102,0.2]
## KRT6B      (0.2,0.297]
## KRT17    (0.493,0.591]
## KRT6A      (0.2,0.297]
## S100A2   (0.786,0.884]
## KRT6C  (0.00302,0.102]

Visualization

We make a histogram for pct.1, pct.2 and avg_logFC.

hist_wc_fun(mark_ORS, "pct.1")

hist_wc_fun(mark_ORS, "pct.2")

Selection

The best markers have high pct.1 and low pct.2 :

mark_ORS = mark_ORS %>%
  dplyr::filter(pct.1 > 0.6 & pct.2 < 0.3)

list_results[[group_name]]$choosen_ones = mark_ORS

mark_ORS

##          p_val avg_logFC pct.1 pct.2 p_val_adj gene_name     pct.1_cut
## KRT16        0  3.892906 0.905 0.155         0     KRT16 (0.905,0.995]
## KRT6B        0  3.166639 0.921 0.275         0     KRT6B (0.905,0.995]
## KRT6A        0  2.860010 0.752 0.260         0     KRT6A (0.726,0.815]
## NDUFA4L2     0  1.721714 0.680 0.212         0  NDUFA4L2 (0.637,0.726]
## TM4SF1       0  1.597284 0.801 0.243         0    TM4SF1 (0.726,0.815]
## LYPD3        0  1.408274 0.732 0.289         0     LYPD3 (0.726,0.815]
##            pct.2_cut
## KRT16    (0.102,0.2]
## KRT6B    (0.2,0.297]
## KRT6A    (0.2,0.297]
## NDUFA4L2 (0.2,0.297]
## TM4SF1   (0.2,0.297]
## LYPD3    (0.2,0.297]

We visualize expression levels of those genes on the projection :

plot_list = lapply(rownames(mark_ORS), FUN = function(one_gene) {
  Seurat::FeaturePlot(sobj, features = one_gene,
                      reduction = name2D) +
    ggplot2::theme(aspect.ratio = 1) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
    Seurat::NoAxes()
})

patchwork::wrap_plots(plot_list, ncol = 3)

IFEb markers

In this section, we compared IFEb to other cells.

group_name = "IFEb_vs_all"

aquarius::plot_red_and_blue(sobj,
                            group1 = "IFE basal",
                            reduction = name2D) +
  ggplot2::labs(title = group_name)

Differential expression

We identify specific markers for each population :

mark = Seurat::FindMarkers(sobj, ident.1 = "IFE basal")

mark = mark %>%
  dplyr::filter(p_val_adj < 0.05) %>%
  dplyr::arrange(-avg_logFC, pct.1 - pct.2)
mark$gene_name = rownames(mark)

list_results[[group_name]]$mark = mark

dim(mark)

## [1] 739   6

head(mark, n = 20)

##                 p_val avg_logFC pct.1 pct.2     p_val_adj gene_name
## KRT15    0.000000e+00 1.2525184 0.929 0.367  0.000000e+00     KRT15
## ATP1B3   0.000000e+00 1.2365428 0.973 0.628  0.000000e+00    ATP1B3
## CCL2     0.000000e+00 1.1973508 0.506 0.056  0.000000e+00      CCL2
## DST      0.000000e+00 1.1908763 0.981 0.352  0.000000e+00       DST
## IMPA2    0.000000e+00 1.0522498 0.924 0.455  0.000000e+00     IMPA2
## COL17A1  0.000000e+00 0.9568857 0.923 0.299  0.000000e+00   COL17A1
## TXNIP    0.000000e+00 0.9062837 0.955 0.547  0.000000e+00     TXNIP
## S100A9   0.000000e+00 0.9041040 0.691 0.231  0.000000e+00    S100A9
## ZFP36L2  0.000000e+00 0.8729918 0.968 0.655  0.000000e+00   ZFP36L2
## IFITM3   0.000000e+00 0.8616994 0.909 0.433  0.000000e+00    IFITM3
## NEAT1    0.000000e+00 0.8281953 0.956 0.672  0.000000e+00     NEAT1
## MOXD1    0.000000e+00 0.8157750 0.764 0.126  0.000000e+00     MOXD1
## CEBPD    0.000000e+00 0.8026200 0.886 0.439  0.000000e+00     CEBPD
## AQP3     0.000000e+00 0.7900901 0.796 0.263  0.000000e+00      AQP3
## PTN     5.159311e-271 0.7884902 0.567 0.234 1.032017e-266       PTN
## ALDH3A1  0.000000e+00 0.7620031 0.607 0.087  0.000000e+00   ALDH3A1
## ARL4A    0.000000e+00 0.7563103 0.874 0.504  0.000000e+00     ARL4A
## GPX2     0.000000e+00 0.7468617 0.633 0.112  0.000000e+00      GPX2
## LIMA1    0.000000e+00 0.7441636 0.893 0.379  0.000000e+00     LIMA1
## FST      0.000000e+00 0.7440017 0.513 0.064  0.000000e+00       FST

How many genes enriched in IFEb ?

mark_IFEb = mark %>%
  dplyr::filter(avg_logFC > 0)

nrow(mark_IFEb)

## [1] 275

We cut pct.1 and pct.2 by bins of 0.1 :

mark_IFEb$pct.1_cut = cut(mark_IFEb$pct.1, breaks = 10)
mark_IFEb$pct.2_cut = cut(mark_IFEb$pct.2, breaks = 10)

head(mark_IFEb)

##         p_val avg_logFC pct.1 pct.2 p_val_adj gene_name     pct.1_cut
## KRT15       0 1.2525184 0.929 0.367         0     KRT15 (0.911,0.999]
## ATP1B3      0 1.2365428 0.973 0.628         0    ATP1B3 (0.911,0.999]
## CCL2        0 1.1973508 0.506 0.056         0      CCL2 (0.479,0.566]
## DST         0 1.1908763 0.981 0.352         0       DST (0.911,0.999]
## IMPA2       0 1.0522498 0.924 0.455         0     IMPA2 (0.911,0.999]
## COL17A1     0 0.9568857 0.923 0.299         0   COL17A1 (0.911,0.999]
##             pct.2_cut
## KRT15   (0.309,0.407]
## ATP1B3  (0.603,0.701]
## CCL2    (0.013,0.112]
## DST     (0.309,0.407]
## IMPA2   (0.407,0.505]
## COL17A1  (0.21,0.309]

Visualization

We make a histogram for pct.1, pct.2 and avg_logFC.

hist_wc_fun(mark_IFEb, "pct.1")

hist_wc_fun(mark_IFEb, "pct.2")

Selection

The best markers have high pct.1 and low pct.2 :

mark_IFEb = mark_IFEb %>%
  dplyr::filter(pct.1 > 0.6 & pct.2 < 0.2)

list_results[[group_name]]$choosen_ones = mark_IFEb

mark_IFEb

##         p_val avg_logFC pct.1 pct.2 p_val_adj gene_name     pct.1_cut
## MOXD1       0 0.8157750 0.764 0.126         0     MOXD1 (0.738,0.825]
## ALDH3A1     0 0.7620031 0.607 0.087         0   ALDH3A1 (0.566,0.652]
## GPX2        0 0.7468617 0.633 0.112         0      GPX2 (0.566,0.652]
## AHNAK2      0 0.7113339 0.749 0.184         0    AHNAK2 (0.738,0.825]
## CDH13       0 0.6071029 0.741 0.158         0     CDH13 (0.738,0.825]
## LAMB3       0 0.6026591 0.740 0.163         0     LAMB3 (0.738,0.825]
## S100A8      0 0.5772416 0.659 0.196         0    S100A8 (0.652,0.738]
## NBL1        0 0.4801919 0.627 0.159         0      NBL1 (0.566,0.652]
## CCDC3       0 0.4355107 0.601 0.182         0     CCDC3 (0.566,0.652]
## SPARC       0 0.4180143 0.607 0.180         0     SPARC (0.566,0.652]
##             pct.2_cut
## MOXD1    (0.112,0.21]
## ALDH3A1 (0.013,0.112]
## GPX2    (0.013,0.112]
## AHNAK2   (0.112,0.21]
## CDH13    (0.112,0.21]
## LAMB3    (0.112,0.21]
## S100A8   (0.112,0.21]
## NBL1     (0.112,0.21]
## CCDC3    (0.112,0.21]
## SPARC    (0.112,0.21]

We visualize expression levels of those genes on the projection :

plot_list = lapply(rownames(mark_IFEb), FUN = function(one_gene) {
  Seurat::FeaturePlot(sobj, features = one_gene,
                      reduction = name2D) +
    ggplot2::theme(aspect.ratio = 1) +
    ggplot2::scale_color_gradientn(colors = aquarius::color_gene) +
    Seurat::NoAxes()
})

patchwork::wrap_plots(plot_list, ncol = 3)

Save

We save the list of results :

saveRDS(list_results, file = paste0(out_dir, "/ors_ifeb_markers.rds"))

We also save as XLSX file :

list_results2 = list(ORS_vs_all = list_results$ORS_vs_all$mark,
                     ORS_vs_all_selection = list_results$ORS_vs_all$choosen_ones,
                     IFEb_vs_all = list_results$IFEb_vs_all$mark,
                     IFEb_vs_all_selection = list_results$IFEb_vs_all$choosen_ones)

openxlsx::write.xlsx(list_results2, file = paste0(out_dir, "/ors_vs_ifeb_markers.xlsx"))

R Session

show

## R version 3.6.3 (2020-02-29)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.6 LTS
## 
## Matrix products: default
## BLAS:   /usr/local/lib/R/lib/libRblas.so
## LAPACK: /usr/local/lib/R/lib/libRlapack.so
## 
## locale:
## [1] C
## 
## attached base packages:
## [1] grid      stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
## [1] ComplexHeatmap_2.14.0 ggplot2_3.3.5         patchwork_1.1.2      
## [4] dplyr_1.0.7          
## 
## loaded via a namespace (and not attached):
##   [1] softImpute_1.4              graphlayouts_0.7.0         
##   [3] pbapply_1.4-2               lattice_0.20-41            
##   [5] haven_2.3.1                 vctrs_0.3.8                
##   [7] usethis_2.0.1               dynwrap_1.2.1              
##   [9] blob_1.2.1                  survival_3.2-13            
##  [11] prodlim_2019.11.13          dynutils_1.0.5             
##  [13] later_1.3.0                 DBI_1.1.1                  
##  [15] R.utils_2.11.0              SingleCellExperiment_1.8.0 
##  [17] rappdirs_0.3.3              uwot_0.1.8                 
##  [19] dqrng_0.2.1                 jpeg_0.1-8.1               
##  [21] zlibbioc_1.32.0             pspline_1.0-18             
##  [23] pcaMethods_1.78.0           mvtnorm_1.1-1              
##  [25] htmlwidgets_1.5.4           GlobalOptions_0.1.2        
##  [27] future_1.22.1               UpSetR_1.4.0               
##  [29] laeken_0.5.2                leiden_0.3.3               
##  [31] clustree_0.4.3              parallel_3.6.3             
##  [33] scater_1.14.6               irlba_2.3.3                
##  [35] markdown_1.1                DEoptimR_1.0-9             
##  [37] tidygraph_1.1.2             Rcpp_1.0.9                 
##  [39] readr_2.0.2                 KernSmooth_2.23-17         
##  [41] carrier_0.1.0               promises_1.1.0             
##  [43] gdata_2.18.0                DelayedArray_0.12.3        
##  [45] limma_3.42.2                graph_1.64.0               
##  [47] RcppParallel_5.1.4          Hmisc_4.4-0                
##  [49] fs_1.5.2                    RSpectra_0.16-0            
##  [51] fastmatch_1.1-0             ranger_0.12.1              
##  [53] digest_0.6.25               png_0.1-7                  
##  [55] sctransform_0.2.1           cowplot_1.0.0              
##  [57] DOSE_3.12.0                 here_1.0.1                 
##  [59] TInGa_0.0.0.9000            ggraph_2.0.3               
##  [61] pkgconfig_2.0.3             GO.db_3.10.0               
##  [63] DelayedMatrixStats_1.8.0    gower_0.2.1                
##  [65] ggbeeswarm_0.6.0            iterators_1.0.12           
##  [67] DropletUtils_1.6.1          reticulate_1.26            
##  [69] clusterProfiler_3.14.3      SummarizedExperiment_1.16.1
##  [71] circlize_0.4.15             beeswarm_0.4.0             
##  [73] GetoptLong_1.0.5            xfun_0.35                  
##  [75] bslib_0.3.1                 zoo_1.8-10                 
##  [77] tidyselect_1.1.0            reshape2_1.4.4             
##  [79] purrr_0.3.4                 ica_1.0-2                  
##  [81] pcaPP_1.9-73                viridisLite_0.3.0          
##  [83] rtracklayer_1.46.0          rlang_1.0.2                
##  [85] hexbin_1.28.1               jquerylib_0.1.4            
##  [87] dyneval_0.9.9               glue_1.4.2                 
##  [89] RColorBrewer_1.1-2          matrixStats_0.56.0         
##  [91] stringr_1.4.0               lava_1.6.7                 
##  [93] europepmc_0.3               DESeq2_1.26.0              
##  [95] recipes_0.1.17              labeling_0.3               
##  [97] httpuv_1.5.2                class_7.3-17               
##  [99] BiocNeighbors_1.4.2         DO.db_2.9                  
## [101] annotate_1.64.0             jsonlite_1.7.2             
## [103] XVector_0.26.0              bit_4.0.4                  
## [105] mime_0.9                    aquarius_0.1.5             
## [107] Rsamtools_2.2.3             gridExtra_2.3              
## [109] gplots_3.0.3                stringi_1.4.6              
## [111] processx_3.5.2              gsl_2.1-6                  
## [113] bitops_1.0-6                cli_3.0.1                  
## [115] batchelor_1.2.4             RSQLite_2.2.0              
## [117] randomForest_4.6-14         tidyr_1.1.4                
## [119] data.table_1.14.2           rstudioapi_0.13            
## [121] org.Mm.eg.db_3.10.0         GenomicAlignments_1.22.1   
## [123] nlme_3.1-147                qvalue_2.18.0              
## [125] scran_1.14.6                locfit_1.5-9.4             
## [127] scDblFinder_1.1.8           listenv_0.8.0              
## [129] ggthemes_4.2.4              gridGraphics_0.5-0         
## [131] R.oo_1.24.0                 dbplyr_1.4.4               
## [133] BiocGenerics_0.32.0         TTR_0.24.2                 
## [135] readxl_1.3.1                lifecycle_1.0.1            
## [137] timeDate_3043.102           ggpattern_0.3.1            
## [139] munsell_0.5.0               cellranger_1.1.0           
## [141] R.methodsS3_1.8.1           proxyC_0.1.5               
## [143] visNetwork_2.0.9            caTools_1.18.0             
## [145] codetools_0.2-16            ggwordcloud_0.5.0          
## [147] Biobase_2.46.0              GenomeInfoDb_1.22.1        
## [149] vipor_0.4.5                 lmtest_0.9-38              
## [151] msigdbr_7.5.1               htmlTable_1.13.3           
## [153] triebeard_0.3.0             lsei_1.2-0                 
## [155] xtable_1.8-4                ROCR_1.0-7                 
## [157] BiocManager_1.30.10         scatterplot3d_0.3-41       
## [159] abind_1.4-5                 farver_2.0.3               
## [161] parallelly_1.28.1           RANN_2.6.1                 
## [163] askpass_1.1                 GenomicRanges_1.38.0       
## [165] RcppAnnoy_0.0.16            tibble_3.1.5               
## [167] ggdendro_0.1-20             cluster_2.1.0              
## [169] future.apply_1.5.0          Seurat_3.1.5               
## [171] dendextend_1.15.1           Matrix_1.3-2               
## [173] ellipsis_0.3.2              prettyunits_1.1.1          
## [175] lubridate_1.7.9             ggridges_0.5.2             
## [177] igraph_1.2.5                RcppEigen_0.3.3.7.0        
## [179] fgsea_1.12.0                remotes_2.4.2              
## [181] scBFA_1.0.0                 destiny_3.0.1              
## [183] VIM_6.1.1                   testthat_3.1.0             
## [185] htmltools_0.5.2             BiocFileCache_1.10.2       
## [187] yaml_2.2.1                  utf8_1.1.4                 
## [189] plotly_4.9.2.1              XML_3.99-0.3               
## [191] ModelMetrics_1.2.2.2        e1071_1.7-3                
## [193] foreign_0.8-76              withr_2.5.0                
## [195] fitdistrplus_1.0-14         BiocParallel_1.20.1        
## [197] xgboost_1.4.1.1             bit64_4.0.5                
## [199] foreach_1.5.0               robustbase_0.93-9          
## [201] Biostrings_2.54.0           GOSemSim_2.13.1            
## [203] rsvd_1.0.3                  memoise_2.0.0              
## [205] evaluate_0.18               forcats_0.5.0              
## [207] rio_0.5.16                  geneplotter_1.64.0         
## [209] tzdb_0.1.2                  caret_6.0-86               
## [211] ps_1.6.0                    DiagrammeR_1.0.6.1         
## [213] curl_4.3                    fdrtool_1.2.15             
## [215] fansi_0.4.1                 highr_0.8                  
## [217] urltools_1.7.3              xts_0.12.1                 
## [219] GSEABase_1.48.0             acepack_1.4.1              
## [221] edgeR_3.28.1                checkmate_2.0.0            
## [223] scds_1.2.0                  cachem_1.0.6               
## [225] npsurv_0.4-0                babelgene_22.3             
## [227] rjson_0.2.20                openxlsx_4.1.5             
## [229] ggrepel_0.9.1               clue_0.3-60                
## [231] rprojroot_2.0.2             stabledist_0.7-1           
## [233] tools_3.6.3                 sass_0.4.0                 
## [235] nichenetr_1.1.1             magrittr_2.0.1             
## [237] RCurl_1.98-1.2              proxy_0.4-24               
## [239] car_3.0-11                  ape_5.3                    
## [241] ggplotify_0.0.5             xml2_1.3.2                 
## [243] httr_1.4.2                  assertthat_0.2.1           
## [245] rmarkdown_2.18              boot_1.3-25                
## [247] globals_0.14.0              R6_2.4.1                   
## [249] Rhdf5lib_1.8.0              nnet_7.3-14                
## [251] RcppHNSW_0.2.0              progress_1.2.2             
## [253] genefilter_1.68.0           statmod_1.4.34             
## [255] gtools_3.8.2                shape_1.4.6                
## [257] HDF5Array_1.14.4            BiocSingular_1.2.2         
## [259] rhdf5_2.30.1                splines_3.6.3              
## [261] AUCell_1.8.0                carData_3.0-4              
## [263] colorspace_1.4-1            generics_0.1.0             
## [265] stats4_3.6.3                base64enc_0.1-3            
## [267] dynfeature_1.0.0            smoother_1.1               
## [269] gridtext_0.1.1              pillar_1.6.3               
## [271] tweenr_1.0.1                sp_1.4-1                   
## [273] ggplot.multistats_1.0.0     rvcheck_0.1.8              
## [275] GenomeInfoDbData_1.2.2      plyr_1.8.6                 
## [277] gtable_0.3.0                zip_2.2.0                  
## [279] knitr_1.41                  latticeExtra_0.6-29        
## [281] biomaRt_2.42.1              IRanges_2.20.2             
## [283] fastmap_1.1.0               ADGofTest_0.3              
## [285] copula_1.0-0                doParallel_1.0.15          
## [287] AnnotationDbi_1.48.0        vcd_1.4-8                  
## [289] babelwhale_1.0.1            openssl_1.4.1              
## [291] scales_1.1.1                backports_1.2.1            
## [293] S4Vectors_0.24.4            ipred_0.9-12               
## [295] enrichplot_1.6.1            hms_1.1.1                  
## [297] ggforce_0.3.1               Rtsne_0.15                 
## [299] shiny_1.7.1                 numDeriv_2016.8-1.1        
## [301] polyclip_1.10-0             lazyeval_0.2.2             
## [303] Formula_1.2-3               tsne_0.1-3                 
## [305] crayon_1.3.4                MASS_7.3-54                
## [307] pROC_1.16.2                 viridis_0.5.1              
## [309] dynparam_1.0.0              rpart_4.1-15               
## [311] zinbwave_1.8.0              compiler_3.6.3             
## [313] ggtext_0.1.0